Implement local search algorithm

src/processing.py

@@ -65,31 +65,39 @@ def get_first_random_solution(m, data):
     return data.iloc[random_indexes]
 
 
-def local_search(n, m, data):
-    solutions = DataFrame(columns=["point", "distance"])
-    first_solution = get_pseudorandom_solution(n=n, data=data)
-    solutions = solutions.append(first_solution, ignore_index=True)
-    for _ in range(m):
-        pass
-    return solutions
 def get_random_solution(previous, data):
     solution = previous.copy()
     worst_index = previous["distance"].astype(float).idxmin()
+    worst_element = previous["distance"].loc[worst_index]
     random_candidate = data.loc[randint(low=0, high=len(data.index))]
-    while (
-        solution.loc[worst_index, "distance"] <= previous.loc[worst_index, "distance"]
-    ):
-        solution.loc[worst_index] = random_candidate
-    return solution
+    while solution["distance"].loc[worst_index] <= worst_element:
+        if random_candidate["distance"] not in solution["distance"].values:
+            solution.loc[worst_index] = random_candidate
+        else:
+            return solution, True
+    return solution, False
 
 
+def explore_neighbourhood(element, data, max_iterations=100000):
+    neighbour = DataFrame()
+    for _ in range(max_iterations):
+        neighbour, stop_condition = get_random_solution(element, data)
+        if stop_condition:
+            break
+    return neighbour
+
+
+def local_search(m, data):
+    first_solution = get_first_random_solution(m=m, data=data)
+    best_solution = explore_neighbourhood(element=first_solution, data=data)
+    return best_solution
 
 
 def execute_algorithm(choice, n, m, data):
     if choice == "greedy":
         return greedy_algorithm(n, m, data)
     elif choice == "local":
-        return local_search(n, m, data)
+        return local_search(m, data)
     else:
         print("The valid algorithm choices are 'greedy' and 'local'")
         exit(1)